An Adaptive Online Learning Model for Flight Data Cluster Analysis

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) Proceedings
PublisherIEEE
ISBN (Electronic)9781538641125
ISBN (Print)9781538641132
Publication statusPublished - Sep 2018

Publication series

NameAIAA/IEEE Digital Avionics Systems Conference - Proceedings
Volume2018-September
ISSN (Print)2155-7195
ISSN (Electronic)2155-7209

Conference

Title37th AIAA/IEEE Digital Avionics Systems Conference (DASC 2018)
PlaceUnited Kingdom
CityLondon
Period23 - 27 September 2018

Abstract

Safety is a top priority for civil aviation. To help airlines further improve safety, various clustering-based methods were developed to better understand their current flight operations and detect unknown risks from onboard flight data. However, existing methods can only be carried on historical data in batches, resulting in its inability to update and adjust as new data come in. New onboard flight data related to anomaly detection are generated at airlines every day. The addition of new data will inevitably cause changes in the clustering results. Yet it would be computational costly to run clustering on all data as they accumulate. Therefore, anomaly detection methods that allow real-time update of cluster models as new data come in are more practical for airlines. This paper presents a reinforcement learning method to identify common patterns in flight data via cluster analysis and update its clusters as new data come in. This method is based on Gaussian Mixture Model (GMM) and uses online (recursive) expectation-maximization (EM) algorithm to update clustering results over time. An initial result of clusters can be obtained by performing GMM-based clustering on historical flight data. Then, as new data come in, the parameters of GMM are updated via an online EM algorithm. By recording the GMM parameters, the method can also track changes in clusters over time. We demonstrated the proposed method using Flight Data Recorder (FDR) data from real operations of an airline. The evolution of clusters was observed as new batches of flight data are fed into the proposed method.

Research Area(s)

  • Gaussian mixture models, adaptive online clustering, expectation maximization, flight data

Citation Format(s)

An Adaptive Online Learning Model for Flight Data Cluster Analysis. / Zhao, Weizun; He, Fang; Li, Lishuai; Xiao, Gang.

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) Proceedings . IEEE, 2018. 8569600 (AIAA/IEEE Digital Avionics Systems Conference - Proceedings; Vol. 2018-September).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review